Visualisations with semantic icons: Assessing engagement with distracting elements

Muna Alebri*, Enrico Costanza, Georgia Panagiotidou, Duncan P. Brumby, Fatima Althani, Riccardo Bovo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

As visualisations reach a broad range of audiences, designing visualisations that attract and engage becomes more critical. Prior work suggests that semantic icons entice and immerse the reader; however, little is known about their impact with informational tasks and when the viewer’s attention is divided because of a distracting element. To address this gap, we first explored a variety of semantic icons with various visualisation attributes. The findings of this exploration shaped the design of our primary comparative online user studies, where
participants saw a target visualisation with a distracting visualisation on a web page and were asked to extract insights. Their engagement was measured through three dependent variables: (1) visual attention, (2) effort to write insights, and (3) self-reported engagement. In Study 1, we discovered that visualisations with semantic icons were consistently perceived to be more engaging than the plain version. However, we found no differences in visual attention and effort between the two versions. Thus, we ran Study 2 using visualisations with more salient semantic icons to achieve maximum contrast. The results were consistent with our first Study. Furthermore, we found that semantic icons elevated engagement with visualisations depicting less interesting
and engaging topics from the participant’s perspective. We extended prior work by demonstrating the semantic value after performing an informational task (extracting insights) and reflecting on the visualisation, besides its value to the first impression. Our findings may be helpful to visualisation designers and storytellers keen on designing engaging visualisations with limited resources. We also contribute reflections on engagement measurements with visualisations and provide future directions.
Original languageEnglish
Article number103343
JournalINTERNATIONAL JOURNAL OF HUMAN COMPUTER STUDIES
Volume191
Early online date6 Aug 2024
DOIs
Publication statusPublished - Nov 2024

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